Systems and methods for intelligent traffic control

ABSTRACT

An intelligent traffic control system may be configured to manage autonomous vehicle traffic, such as by communicating with autonomous vehicles. The intelligent traffic control system may be configured to output an indication of a traffic control command to autonomous vehicles and non-autonomous vehicles. The intelligent traffic control system may be configured to determine traffic control commands for autonomous vehicles and non-autonomous vehicles.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims priority to and the benefit ofprovisional patent application 62/882,751 filed Aug. 5, 2019, which isincorporated herein by reference in its entirety.

BACKGROUND

The adoption of autonomous vehicles may present several challenges totraditional traffic control systems. As non-autonomous vehicles maystill be used and autonomous vehicles may have manned modes, a need fortraffic control systems may remain. However, traditional traffic controlsystems may not be configured to communicate with autonomous vehicles.Furthermore, traditional traffic control systems may not be configuredto make traffic control decisions for autonomous vehicle traffic.Therefore, new and more intelligent traffic control systems are needed.

SUMMARY

An intelligent traffic control system may be configured to manageautonomous vehicle traffic, such as by communicating with autonomousvehicles. To coordinate autonomous vehicle traffic and non-autonomousvehicle traffic, including autonomous vehicles operating in mannedmodes, the intelligent traffic control system may be configured tooutput an indication of a traffic control command to autonomous vehiclesand to non-autonomous vehicles.

An intelligent traffic control system may be configured to determinetraffic conditions, such as traffic conditions on a road havingautonomous vehicle and/or non-autonomous vehicle traffic. Theintelligent traffic control system may be configured to communicatetraffic conditions associated with non-autonomous vehicles to autonomousvehicles. Based on the traffic conditions, the intelligent trafficcontrol system may be configured to determine a traffic control commandfor vehicles on the road.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings show generally by way of example, but not by wayof limitation, various examples discussed in the present disclosure. Inthe drawings:

FIG. 1 shows an example intelligent traffic control system.

FIG. 2 shows an example traffic control environment.

FIG. 3 shows an example intelligent traffic control method.

FIG. 4 shows an example intelligent traffic control method.

FIG. 5 shows an example computing environment.

DETAILED DESCRIPTION

As the presence of autonomous vehicles on public roadways increases,there may arise a need to restructure traffic control systems. Thepresent disclosure describes an improved, intelligent traffic controlsystem. The traffic control system may be configured to communicate withautonomous vehicles, as autonomous vehicles may follow traffic patternsbased on internal navigation systems. For example, the traffic controlsystem may communicate with autonomous vehicles to issue a trafficcontrol command to the vehicles. The traffic control system may outputan indication of the traffic control command via a traffic controldevice for operators of non-autonomous vehicles and/or autonomousvehicles operating in manned modes that are sharing the roadway with theautonomous vehicles. As another example, the traffic control system maycommunicate with autonomous vehicles to share information regardingnon-autonomous vehicle traffic. The information regarding non-autonomousvehicle traffic may inform the navigation decisions of the autonomousvehicles. As yet another example, the traffic control system maydetermine a traffic control command based on autonomous vehicle andnon-autonomous vehicle traffic.

Furthermore, challenges may be presented by the sharing of roadways byautonomous vehicles and non-autonomous vehicles. Similarly, challengesmay be presented by the sharing of roadways by autonomous vehiclesoperating in manned modes and autonomous vehicles operating in unmannedmodes. Although autonomous vehicles may be configured to communicatewith each other and/or with a central network and to make trafficdecisions based on shared information, operators of non-autonomousvehicles or autonomous vehicles in manned modes may not be privy to theshared information and may not have the benefit of the information whenmaking traffic decisions. Operators may still be reliant on visual cues,such as the sight of surrounding traffic and the sight of commandsoutput by traffic control devices. As a result, there may be a lack ofsynchronization between autonomous vehicle and non-autonomous vehicletraffic. Therefore, the improved traffic control system may beconfigured to communicate with autonomous vehicles and operators ofnon-autonomous and/or manned mode vehicles. The improved traffic controlsystem may be configured to output an indication of a traffic controlcommand to autonomous vehicles and to operators of non-autonomous and/ormanned mode vehicles.

FIG. 1 shows an intelligent traffic control system 100. The intelligenttraffic control system 100 may comprise a plug-and-play systemconfigured to work with existing traffic control cabinets, controllers,or other components. The intelligent traffic control system 100 maycomprise one or more sensors 101. The sensor 101 may comprise anintrusive sensor. The intrusive sensor may be installed on a surface,such as of a road or adjacent a road. The intrusive sensor may comprisea magnetic sensor, a piezolelectric sensor, a pneumatic tube sensor, oran inductive coil loop, as examples.

The sensor 101 may comprise a non-intrusive sensor. The non-intrusivesensor may be disposed on and/or over a surface. For example, thenon-intrusive sensor may be mounted on a mast or a bridge. Thenon-intrusive sensor may be located at ground level, such as on aroadside. The non-intrusive sensor may comprise a camera (e.g., a videocamera), a radar sensor, an infrared sensor, an ultrasonic sensor, anacoustic sensor (e.g., acoustic array sensors), a road surface conditionsensor, and/or a radio-frequency identification (RFID) sensor, asexamples.

The sensor 101 may be configured to generate data associated withtraffic. The sensor 101 may be configured to generate data associatedwith a coverage area (e.g., area 201 in FIG. 2 ) and/or traffic in thecoverage area. The sensor 101 may be configured to send the data toanother device wirelessly or via a wired medium.

The intelligent traffic control system 100 may comprise a vehicle 113.The vehicle 113 may comprise a car, a truck, a motorcycle, a motorscooter, an electric bike, an electric cart, an all-terrain vehicles(ATV's), and/or an aerial vehicle, as examples. The vehicle 113 maycomprise a non-autonomous vehicle. The vehicle 113 may comprise anautonomous vehicle. The autonomous vehicle may have an unmanned mode.The autonomous vehicle may have a manned mode. An operator of theautonomous vehicle may switch between a manned mode and an unmannedmode.

The vehicle 113 may comprise one or more vehicle sensors 114. Thevehicle sensor 114 may comprise a safety sensor. The safety sensor maybe configured to generate data associated with accident hazards andtraffic events, such as in real-time. The safety sensor may comprise amicro-mechanical oscillator, a speed sensor, a camera, a radar beam, alaser beam, a Light Detection and Ranging (LIDAR) sensor, an inertialsensor, an ultrasonic sensor, a proximity sensor, a night vision sensor,and/or a haptic sensor, as examples.

The vehicle sensor 114 may comprise a diagnostic sensor. The diagnosticsensor may be configured to generate data associated with a statusand/or performance of the vehicle 113, such as for detectingmalfunctioning of the vehicle 113. The diagnostic sensor may comprise aposition sensor, a chemical sensor, a temperature sensor, a gascomposition sensor, a pressure sensor, and/or an airbag sensor, asexamples.

The vehicle sensor 114 may comprise a traffic sensor. The traffic sensormay be configured to generate data associated with traffic conditionsand/or management in an area (e.g., area 201 in FIG. 2 ). The trafficsensor may comprise a camera, a radar sensor, a LIDAR sensor, anultrasonic sensor, and/or a proximity sensor, as examples.

The vehicle sensor 114 may comprise an assistance sensor. The assistancesensor may be configured to generate data associated with comfort orconvenience, such as of an operator or a passenger of the vehicle 112.The assistance sensor may comprise a gas composition sensor, a humiditysensor, a temperature sensor, a position sensor, a torque sensor, animage sensor, a rain sensor, a fogging prevention sensor, and/or adistance sensor, as examples.

The vehicle sensor 114 may comprise an environment sensor. Theenvironment sensor may be configured to generate data associated withenvironmental conditions in the vehicle. The environment sensor maycomprise a pressure sensor, a temperature sensor, a distance sensor, acamera, and/or a weather condition sensor, as examples.

The vehicle sensor 114 may comprise a user sensor. The user sensor maybe configured to generate data associated with health and/or behavior ofan operator and/or a passenger of the vehicle 112. The user sensor maycomprise a camera, a thermistor, an eletrocardiogram (ECG) sensor, anelectroencephalogram (EEG) sensor, and/or a heart rate sensor, asexamples.

The vehicle 113 may comprise a vehicle communication module 115. Thevehicle communication module 115 may be configured to communicate withanother vehicle, such as via a communication module of the othervehicle. The vehicle communication module 115 may be configured tocommunicate with a satellite, such as a Global Positioning System (GPS)satellite. The vehicle communication module 115 may be configured tocommunicate using Wi-Fi, Bluetooth, Radio Frequency (RF), cellular,broadband, dedicated short range communication (DSRC), IEEE 802.11p,C-V2X/cellular V2X, local interconnect network (LIN), time-triggeredlight weight protocol (TTP/A), controller area network-bus (CAN-B)protocol, J1850 protocol, media oriented system transport (MOST),digital data bus, Flexray, ZigBee, Ultra-wideband (UWB), and/or anothercommunication protocol. The vehicle communication module 115 may beconfigured to communicate via a network 105. The network 105 maycomprise a wide area network (WAN), a local area network (LAN), a plaintelephone service network (PTSN), or another kind of network.

The vehicle communication module 115 may be configured to output anindication of data generated by the vehicle sensor 114. As an example,the vehicle communication module 115 may send an indication of data fromthe vehicle sensor 114 to another vehicle. As another example, thevehicle communication module 115 may send an indication of data from thevehicle sensor 114 to a cloud computing network.

Data from various vehicles may be aggregated in the cloud computingnetwork. Features and/or characteristics of the vehicles may be known ordetermined, such as by one or more cloud computing devices. The featuresand/or characteristics of the vehicles may comprise size, weight, and/ormodel, as examples. Based on the data from the various vehicles and/orthe features and/or characteristics of the vehicles, one or more cloudcomputing devices may generate one or more maps. The map may comprise atwo-dimensional map or a three-dimensional map. The map may compriseenvironmental data. The map may comprise localization data.

The map may be sent to one or more vehicles, such as to the vehicle 113.Based on the map, the vehicle 113 may be configured to make navigationdecisions. For example, the vehicle 113 may comprise a computing module116. The computing module 116 may comprise memory configured to storeinstructions, sensor data, communicated data, and/or the map. Thecomputing module 116 may comprise one or more processors configured toexecute the instructions. The instructions, when executed by the one ormore processors, make cause the computing module 116 to make navigationdecisions based on the map.

The intelligent traffic control system 100 may comprise a computingdevice 102. The computing device 102 may comprise memory configured tostore instructions and/or other data. The computing device 102 maycomprise a processor configured to execute the instructions. Thecomputing device 102 may comprise a server. The computing device 102 maycomprise a cloud computing device, such as a node in a cloud computingnetwork configured to receive and aggregate data from vehicles. Thecomputing device 205 may comprise a controller, such as anelectro-mechanical controller, a signal controller, or a solid-statecontroller.

The computing device 102 may be configured to receive data from thesensor 101. The computing device 102 may be configured to receive datafrom the vehicle 113. The computing device 102 may be configured toreceive data from the sensor 101 and/or the vehicle 113 via the network105. The computing device 102 may be configured to receive data from thesensor 101 and/or the vehicle 113 via a repeater 103.

The computing device 102 may be configured to communicate with userdevices, such as mobile phones, tablet devices, wearable devices,personal computers, and/or internet of things (IoT) devices. Thecomputing device 102 may be configured to communicate with anothercomputing device. The computing device 102 may be configured tocommunicate with one or more autonomous vehicles, such as the vehicle113. The computing device 102 may be configured to communicate usingWi-Fi, Bluetooth, Radio Frequency (RF), cellular, broadband, dedicatedshort range communication (DSRC), IEEE 802.11p, C-V2X/cellular V2X,local interconnect network (LIN), time-triggered light weight protocol(TTP/A), controller area network-bus (CAN-B) protocol, J1850 protocol,media oriented system transport (MOST), digital data bus, Flexray,ZigBee, Ultra-wideband (UWB), and/or another communication protocol. Thecomputing device 102 may be configured to communicate via the network105. The computing device 102 may be configured to communicate via awired communication medium.

The computing device 102 may be configured to determine a trafficcondition, such as in an area (e.g., area 201 in FIG. 2 ). The trafficcondition may comprise a presence, a number of vehicles, a size (e.g., alength, width, height, weight, etc.), a direction of travel, and/or aspeed of vehicles, such as in the area. The traffic condition maycomprise a presence of pedestrians and/or number of pedestrians in thearea. The traffic condition may comprise a number of autonomousvehicles, a number of non-autonomous vehicles, a number of autonomousvehicles operating in a manned mode, and/or a number of autonomousvehicles operating in an unmanned mode, such as in the area. Thecomputing device 102 may be configured to determine the trafficcondition based on data received from the sensor 101, the vehicle 113,another computing device and/or another source, such as a database.

The computing device 102 may be configured to determine a trafficcontrol command. The traffic control command may be for vehicles and/orpedestrians in the area. The traffic control command may comprise a stopcommand (e.g., a red light), a go command (e.g., a green light), a yieldcommand (e.g., a yellow light or a flashing light), a cross command,and/or a slow command, as examples.

The computing device 102 may be configured to determine differenttraffic control commands for particular vehicles. For example, thecomputing device 102 may determine to command a first device to go and asecond device to stop. The computing device 102 may determine to commanda vehicle operating in a manned mode to switch to an unmanned mode, suchas to override a human's operating of the vehicle. For example, thecomputing device 102 may determine to command a vehicle to switch to anunmanned mode based on dangerous driving behavior. Once the vehicle isoperating in the unmanned mode, the computing device 102 may determine acommand configured to cause the vehicle to stop in a safe place, to slowdown, and/or to operate differently. The computing device 102 maydetermine to command a vehicle operating in an unmanned mode to switchto a manned mode.

The computing device 102 may be configured to determine the trafficcontrol command based on the traffic condition and/or other data. Theother data may comprise data received from the sensor 101, the vehicle113, another computing device and/or another source, such as a database.The computing device 102 may be configured to determine the trafficcontrol command using a method similar to method 400 in FIG. 4 .

The computing device may be configured to output an indication of thetraffic control command. The computing device may output the indicationof the traffic control command to the vehicle 113, to another computingdevice, and/or to a traffic control device 104. The computing device mayoutput the indication of the traffic control command via the repeater103. The computing device may be configured to cause the traffic controldevice 104 to output an indication of the traffic control command.

The intelligent traffic control system 100 may comprise a trafficcontrol device 104. The traffic control device 104 may comprise all or aportion of the computing device 102. The traffic control device 104 maycomprise all or a portion of the sensor 101. The traffic control device104 may comprise a mobile device, such as a handheld device and/or auser device. The traffic control device 104 may comprise an in-vehicledevice, such as a portion of a dashboard of a vehicle and/or a deviceconfigured to be mounted on a center console or windshield of a vehicle.The traffic control device 104 may comprise a roadside device, such as adevice configured to be deployed on a road, along a road, and/or over aroad. The traffic control device 104 may be self-mobile and/orself-propelling, such as a terrestrial drone or an aerial drone.

The traffic control device 104 may comprise a communication module 106.The communication module 106 may be configured to communicate with thevehicle 113. The communication module 106 may be configured tocommunicate with another traffic control device. The communicationmodule 106 may be configured to communicate with user devices, such asmobile phones, tablet devices, wearable devices, personal computers,and/or internet of things (IoT) devices. The communication module 106may be configured to communicate with the computing device 102. Thecommunication module 106 may be configured to communicate with thesensor 101. The communication module 106 may be configured tocommunicate with the repeater 103. The communication module 106 may beconfigured to communicate with one or more devices associated withgovernment authorities and/or emergency services.

The communication module 106 may comprise a receiver, repeater,transceiver, and/or transmitter. The communication module 106 may beconfigured to communicate using Wi-Fi, Bluetooth, Radio Frequency (RF),cellular, broadband, dedicated short range communication (DSRC), IEEE802.11p, C-V2X/cellular V2X, local interconnect network (LIN),time-triggered light weight protocol (TTP/A), controller areanetwork-bus (CAN-B) protocol, J1850 protocol, media oriented systemtransport (MOST), digital data bus, Flexray, ZigBee, Ultra-wideband(UWB), and/or another communication protocol. The communication module106 may be configured to communicate via the network 105. Thecommunication module 106 may be configured to communicate via a wiredcommunication medium.

The traffic control device 104 may comprise a display module 107. Thedisplay module 107 may comprise a screen. The traffic control device 104may be configured to output an indication of a traffic control commandvia the screen. The traffic control command output via the screen may befor vehicle operators and/or pedestrians. The indication of the trafficcontrol command may comprise text. For example, the indication of thetraffic control command may comprise the word “go.” The indication ofthe traffic control command may comprise a graphic representation. Thegraphic representation may comprise a symbol, such as a red polygon oran open palm representing a “stop” command. The graphic representationmay comprise an animation, such as an animation of a walking figurerepresenting a “cross” command.

The traffic control device 104 may be configured to output an indicationof a warning or information for vehicle operators and/or pedestrians viathe screen. The warning or information may comprise a speed limit, anindication of a direction of a travel of a lane and/or other portion ofa road, information about traffic density, information about anaccident, information about weather, indications of road closures orblocks, and/or indications of pot holes, as examples.

The display module 107 may comprise a light source. The traffic controldevice 104 may be configured to signal vehicle operators and/orpedestrians using the light source. As an example, the light source mayoutput light in a range of colors, such as red for “stop,” yellow for“slow,” and green for “go.” The intensity and/or brightness of the lightsource may be determined and/or changed, such as based on the time ofday, ambient lighting, weather, location of the traffic control device104, and/or traffic density.

The traffic control device 104 may comprise an audio module 108. Theaudio module 108 may comprise an audio output device, such as a speaker.The speaker may be configured to output audio indicating a trafficcontrol command, a warning, and/or information to vehicle operatorsand/or pedestrians. The commands, warnings, or information may comprisewords, music, and/or sounds, as examples. For example, the speaker mayoutput the word “cross” or music when pedestrians may cross a road. Asanother example, the speaker may output the word “wait,” differentmusic, or an alarm bell when pedestrians should not cross the road.

The audio module 108 may comprise an audio input device, such as amicrophone or an audio recording device. The audio input device mayreceive and/or record sound. Based on the received sound, the trafficcontrol device 104 may be configured to execute one or more operations.For example, based on a sound, such as a sound of a vehicle accidentbeing beyond a threshold volume, the traffic control device 104 may senda notification to emergency responders.

The traffic control device 104 may comprise a mobility module 109. Themobility module 109 may be configured to propel and/or move the trafficcontrol device 104. The mobility module 109 may comprise an engine. Themobility module 109 may comprise a motor. The mobility module 109 may beconfigured to move the traffic control device 104 on the ground. Forexample, the mobility module 109 may comprise wheels and/or tires. Themobility module 109 may be configured to move the traffic control device104 in the air. For example, the mobility module 109 may comprise apropeller and/or wings. The traffic control device 104 may be configuredto cause the mobility module 109 to move the traffic control device 104to a determined location, a determined distance, or in a determineddirection, as examples.

The traffic control device 104 may comprise a computing module 110. Thecomputing module 110 may comprise one or more processors. The computingmodule 110 may comprise a memory. The memory may store instructions.When executed by the one or more processors, the instructions may causethe traffic control device 104 to perform operations. The operations maycomprise communicating using the communication module 106. Theoperations may comprise outputting an indication of a traffic controlcommand, a warning, and/or information via the display module 107. Theoperations may comprise receiving, recording, and/or outputting soundusing the audio module 108. The operations may comprise moving thetraffic control device 104 using the mobility module 109.

The traffic control device 104 may be configured to determine a trafficcondition, such as in an area (e.g., area 201 in FIG. 2 ). The trafficcontrol device 104 may be configured to determine the traffic conditionbased on data received from the sensor 101, the vehicle 113, anothercomputing device and/or another source, such as a database.

The traffic control device 104 may be configured to determine a trafficcontrol command. The traffic control device 104 may be configured todetermine the traffic control command based on the traffic conditionand/or other data. The other data may comprise data received from thesensor 101, the vehicle 113, the computing device 102, and/or anothersource, such as a database. The traffic control device 104 may beconfigured to determine the traffic control command using a methodsimilar to method 400 in FIG. 4 .

The traffic control device 104 may comprise a power module 111. Thepower module 111 may be configured to supply power to one or morecomponents of the traffic control device 104, such as the communicationmodule 106, the display module 107, the audio module 108, the computingmodule 110, and/or the mobility module 109. The power module 111 maycomprise a battery. The battery may comprise a replaceable battery. Thebattery may comprise a rechargeable battery. For example, the batterymay be recharged using electricity or solar energy. The power module 111may receive power from a source external to the traffic control device104.

The power module 111 may be configured operate in a power-saving mode,such as a sleep mode. For example, the power module 111 may beconfigured to operate in the power-saving mode based on a density oftraffic in an area or a time of day. The power module 111 may beconfigured to switch from the power-saving mode to a higher power mode,such as a wake mode. For example, the power module 111 may switch powermodes based on a time of day, a density of traffic, receiving data,and/or detecting motion, sound, and/or light.

The traffic control device may comprise a body 112. The body 112 mayhouse the communication module 106, the display module 107, the audiomodule 108, the computing module 110, the power module 111, and/or themobility module 109. The body 112 may comprise a hard casing, such as ashell, a cabinet, or a box. The body 112 may be configured to protectthe communication module 106, the display module 107, the audio module108, the computing module 110, the power module 111, and/or the mobilitymodule 109. The body 112 may be coupled to and/or disposed on themobility module 109. For example, the mobility module 109 may beconfigured to carry and/or move the body 112. The body 112 may bemounted on a support structure, such as a pole, a wire, a cable, or abar, as examples. The body 112 may be configured to be installed in avehicle. The body 112 may be configured to be mounted and/or installedon a dashboard, a console, a windshield, and/or a window of a vehicle,as examples. The body 112 may be configured to be mobile, such as to becarried by a user.

The body 112 may be connected to the power module 111. For example thebody 112 may receive power from the power module 111 at a connectionpoint on the body 112. The body 112 may be configured to connect to anexternal power source. For example, the body 112 may comprise aconnection point configured to connect to a generator, a solar panel,and/or a power outlet. The body 112 may be configured to supply powerfrom the external power source to the power module 111.

FIG. 2 shows an example operating environment 200. The operatingenvironment 200 may comprise an area 201. The area 201 may comprise anyarea in which traffic may be controlled. As examples, the area 201 maycomprise a square mile, a square quarter mile, or a square eighth mile.The area 201 may comprise a portion of a road. The road may comprise aone-way road or a two-way road, as examples. Although area 201 is shownas a road with two lanes in FIG. 2 , the road may comprise any number oflanes (e.g., one lane, three lanes, etc.). The road may comprise astreet, a path, a highway, and/or a freeway, as examples.

The area 201 may comprise an intersection of roads or lanes (e.g., anintersection of two roads, three roads, four roads, etc.), an areaadjacent to an intersection, and/or an area between two or moreintersections, as examples. The area 201 may comprise one or more bikelanes. The area 201 may comprise one or more sidewalks or pedestrianareas. The area 201 may comprise an area between two traffic controldevices. The area 201 may comprise an area that is within view and/orrange of a camera (e.g., a camera configured to gather traffic dataand/or a camera of a traffic control device).

The area 201 may be defined by an area where one or more sensors 206 arelocated. For example, the area 201 may encompass an array of sensors 206or a plurality of sensors that generate data used to determine a trafficcontrol command. The area 201 may comprise an area that is withindetection range of one or more sensors 206.

The operating environment 200 may comprise one or more vehicles 202.Although FIG. 2 shows three vehicles 202 in the area 201, there may beany number of vehicles 202 in the area 201. The vehicle may be similarto vehicle 113 in FIG. 1 . The vehicle 202 may be in communication witha sensor 206, a computing device 205, another vehicle 202, and/or atraffic control device 204.

The operating environment 200 may comprise one or more sensors 206.Although FIG. 2 shows one sensor 206 in the area 201, there may be anynumber of sensors 206 in the area 201. The sensors 206 may be similar tothe sensor 101 in FIG. 1 . The sensors 206 may be located in the area201. The sensors 206 may be located outside the area 201. The sensors206 may be located in another area adjacent to the area 201. The sensor206 may be configured to gather data associated with the area and/ortraffic in the area 201. The sensor 206 may be in communication with thevehicle 202, a computing device 205, and/or a traffic control device204.

The operating environment 200 may comprise one or more computing devices205. The computing device 205 may be similar to the computing device 102in FIG. 1 . The computing device 205 may be located in the area 201. Thecomputing device 205 may be located outside the area 201. The computingdevice 205 may be located in another area adjacent to the area 201. Thecomputing device 205 may be in communication with the vehicle 202, thesensor 206, another computing device 205, and/or a traffic controldevice 204.

The operating environment 200 may comprise one or more traffic controldevices 204. The traffic control device 204 may be similar to thetraffic control device 104 in FIG. 1 . The traffic control device 204may be associated with the area 201. For example the traffic controldevice 204 may determine and/or output a traffic control command fortraffic in the area 201. As another example, a first traffic controldevice 204 may determine and/or output traffic control commands fortraffic going in one direction on a road and a second traffic controldevice 204 may determine and/or output traffic control commands fortraffic going in another direction on the road. The traffic controldevice 204 may be in communication with the vehicle 202, the sensor 206,the computing device 205, and/or another traffic control device 204. Thevehicle 202, the computing device 205, and/or the traffic control device204 may be configured to communicate via a network 203. The network 203may be similar to the network 105 in FIG. 1 .

The computing device 205 and/or the traffic control device 204 may beconfigured to determine a traffic condition associated with the area201. The computing device 205 and/or the traffic control device 204 maydetermine the traffic condition based on data received from the sensor206, the vehicle 202, another computing device 205, another trafficcontrol device 204, and/or another source, such as a database.

The computing device 205 and/or the traffic control device 204 may beconfigured to output an indication of the traffic condition. Forexample, an indication of a traffic condition associated withnon-autonomous vehicles 202 may be communicated to one or moreautonomous vehicles 202. An indication of a traffic condition associatedwith autonomous vehicles 202 may be communicated to one or morenon-autonomous vehicles 202. An indication of a traffic conditionassociated with autonomous vehicles 202 operating in an unmanned modemay be communicated to one or more autonomous vehicles 202 operating ina manned mode. An indication of a traffic condition associated withautonomous vehicles 202 operating in a manned mode may be communicatedto one or more autonomous vehicles 202 operating in an unmanned mode.

The computing device 205 and/or the traffic control device 204 may beconfigured to determine a traffic control command. The traffic controlcommand may be associated with the area 201, such as for pedestrians orvehicles 202 in the area 201. The computing device 205 and/or thetraffic control device 204 may determine the traffic control commandbased on the traffic condition. The computing device 205 and/or thetraffic control device 204 may determine the traffic control commandbased on data received from the sensor 206, the vehicle 202, anothercomputing device 205, another traffic control device 204, and/or anothersource, such as a database. The traffic control command may bedetermined using a method similar to method 400 in FIG. 4 .

The traffic control device 204 may be configured to output an indicationof the traffic control command. The traffic control device 204 mayoutput an indication of the traffic control command to one or moreautonomous vehicles 202 and/or autonomous vehicles 202 operating inunmanned modes by sending an indication of the traffic control commandto the autonomous vehicles. The traffic control device 204 may be outputan indication of the traffic control command to one or more operators ofone or more non-autonomous vehicles 202 and/or autonomous vehicles 202operating in a manned mode. The traffic control device 204 may beconfigured to output an indication of the traffic control command to theautonomous vehicles 202 and/or autonomous vehicles 202 operating in anunmanned mode and simultaneously output an indication of the trafficcontrol command to the operators of the non-autonomous vehicles 202and/or the autonomous vehicles 202 operating in a manned mode. As aresult, traffic between autonomous vehicles and non-autonomous vehiclesmay be aligned. For example, the navigation system of an autonomousvehicle may update a driving course and/or pattern based on the trafficcontrol command. An operator may make a driving decision based on thetraffic control command.

FIG. 3 shows an example method 300. At step 310, data associated withtraffic may be received and/or generated. The data may be generated byand/or received from a sensor (e.g., sensor 101 in FIG. 1 and/or sensor206 in FIG. 2 ), a database, a vehicle (e.g., vehicle 113 in FIG. 1and/or vehicle 202 in FIG. 2 ), a repeater (e.g., repeater 103 in FIG. 1), a computing device (e.g., computing device 102 in FIG. 1 and/orcomputing device 205 in FIG. 2 ), and/or a traffic control device (e.g.,traffic control device 104 in FIG. 1 and/or traffic control device 204in FIG. 2 ). The data may be generated by and/or received from a cloudcomputing network, such as a cloud computing network that communicatewith autonomous vehicles. The data may be received by a computing device(e.g., computing device 102 in FIG. 1 and/or computing device 205 inFIG. 2 ) and/or a traffic control device (e.g., traffic control device104 in FIG. 1 and/or traffic control device 204 in FIG. 2 ). The trafficdata may be received from both sensors and one or more vehicles orvehicles systems (e.g., networks communicating with vehicles and/oraggregating data from vehicles).

The data associated with traffic may be associated with traffic in anarea, such as the area 201 in FIG. 2 . The data may comprise a sensorreading, such as a measurement of a magnetic field of an induction loop,an air pressure measurement, a laser measurement, and/or a radarmeasurement. The data may comprise image data. The image data maycomprise one or more still images. The image data may comprise video.The image may comprise an aerial image, a ground-level image, or aneye-level image. The image may comprise an image captured from a heightand/or position of a camera and/or a traffic control device (e.g., thetraffic control device 104 in FIG. 1 ).

At step 320, a traffic condition associated with one or morenon-autonomous vehicles may be determined. The non-autonomous vehiclemay be similar to vehicle 113 in FIG. 1 and/or vehicle 202 in FIG. 2 .The non-autonomous vehicle may be in an area (e.g., area 201 in FIG. 2). The traffic condition may be determined by the computing deviceand/or the traffic control device. The traffic condition may bedetermined based on the traffic data. The traffic condition may bedetermined based on both traffic data from one or more sensors andtraffic data from a vehicle or a vehicle system.

The traffic condition may comprise a number of vehicles in the area. Asexamples, the number of vehicles in the area may be determined based ontraffic data from a pneumatic road tube, inductive loop detector,magnetic sensor, ultrasonic sensor, acoustic array sensor, and/orpiezoelectric sensor, as examples. The number of vehicles in the areamay be determined based on traffic image data.

The traffic condition may comprise a number of the vehicles in the areathat are non-autonomous. The non-autonomous vehicles may be identifiedbased on makes and/or models of the vehicles, license plates on thevehicles, and/or driving patterns of the vehicles. For example, theautonomous vehicles may follow straighter paths or may travel with moreconsistent velocity than non-autonomous vehicles. As another example,autonomous vehicles may exhibit more consistent braking time or smootherbraking than non-autonomous vehicles. The makes and/or models of thevehicles, license plates on the vehicles, and/or driving patterns of thevehicles may be used to determine if the vehicles are autonomous and/ornon-autonomous.

The non-autonomous vehicles may be determined based on data indicativeof a number of autonomous vehicles in the area. For example, a number ofautonomous vehicles, map of autonomous vehicles, and/or other data maybe received from a cloud computing network that tracks autonomousvehicles and/or aggregates data from autonomous vehicles. Vehicles maycomprise tracking devices that allow GPS coordinates of their locationsto be determined. Based on the locations of the vehicles, it may bedetermined which autonomous vehicles and/or non-autonomous vehicles arein the area. The number of autonomous vehicles may be subtracted fromthe number of total vehicles to determine the number of non-autonomousvehicles.

The non-autonomous vehicles may be determined based on communicatingwith one or more vehicles. For example, signals may be sent to thevehicles in the area. The autonomous vehicles may respond to thesignals. Based on vehicles not responding to the signals, it may bedetermined that the vehicles are non-autonomous.

The traffic condition may comprise a speed of travel, direction oftravel, and/or trajectory of travel of the non-autonomous vehicle, asexamples. For example, the traffic condition may comprise a trajectoryof one or more non-autonomous vehicles. For example, a time-spacediagram may be generated (e.g., based on positions, velocities, and/oraccelerations) for one or more non-autonomous vehicles. The trafficcondition may comprise a size (e.g., a length, width, height, weight,etc.) of the non-autonomous vehicles. For example, the size of thenon-autonomous vehicles may be determined based on traffic data receivedfrom a piezoelectric and/or infrared sensor.

At step 330, an indication of the traffic condition associated with thenon-autonomous vehicle may be output. The indication of the trafficcondition may be sent to one or more autonomous vehicles. The autonomousvehicle may be similar to vehicle 113 in FIG. 1 and/or vehicle 202 inFIG. 2 . The autonomous vehicle may be in the same area as thenon-autonomous vehicle. The autonomous vehicle may be in a differentarea as the non-autonomous vehicle, such as an area in which thenon-autonomous vehicle will enter or an area adjacent to the area of thenon-autonomous vehicle. The indication of the traffic condition may beoutput by the computing device and/or the traffic control device. Basedon the traffic condition, the autonomous vehicle may determine and/orupdate a driving pattern or driving decision.

The indication of the traffic condition may be sent to a cloud computingnetwork. The cloud computing network may aggregate the information aboutthe traffic condition with other traffic data. Based on the aggregateddata, the cloud computing network may generate a map. The autonomousvehicle may receive the map. The autonomous vehicle may make a trafficdecision and/or follow a traffic pattern based on the map. Based on theaggregated data, the cloud computing network may determine a trafficdecision and/or a traffic pattern for the autonomous vehicle. Theautonomous vehicle may receive an indication of the traffic decisionand/or the traffic pattern and may obey the traffic decisions and/or thetraffic pattern.

Steps similar to steps 310-330 may be used to determine a trafficcondition associated with one or more autonomous vehicles operating in amanned mode. An indication of the traffic condition may be sent to oneor more autonomous vehicles operating in an unmanned mode and/or a cloudcomputing network.

Steps similar to steps 310-330 may be used to determine a trafficcondition associated with an autonomous vehicle. An indication of thetraffic condition may be sent to a non-autonomous vehicle and/or adevice associated with an operator of a non-autonomous vehicle, such asa mobile device. An indication of the traffic condition may be outputvia the traffic control device. For example, the indication of thetraffic condition may be output via a screen of the traffic controldevice, which may comprise a mobile device, an in-vehicle device, or aroadside device. The indication of the traffic condition may be outputaudibly via a speaker of the traffic control device.

FIG. 4 shows an example method 400. At step 410, data associated withtraffic may be received and/or generated. The data may be generated byand/or received from a sensor (e.g., sensor 101 in FIG. 1 and/or sensor206 in FIG. 2 ), a database, a vehicle (e.g., vehicle 113 in FIG. 1and/or vehicle 202 in FIG. 2 ), a repeater (e.g., repeater 103 in FIG. 1), a computing device (e.g., computing device 102 in FIG. 1 and/orcomputing device 205 in FIG. 2 ), and/or a traffic control device (e.g.,traffic control device 104 in FIG. 1 and/or traffic control device 204in FIG. 2 ). The data may be generated by and/or received from a cloudcomputing network, such as a cloud computing network that communicatewith autonomous vehicles. The data may be received by a computing device(e.g., computing device 102 in FIG. 1 and/or computing device 205 inFIG. 2 ) and/or a traffic control device (e.g., traffic control device104 in FIG. 1 and/or traffic control device 204 in FIG. 2 ).

The data associated with traffic may be associated with traffic in anarea, such as the area 201 in FIG. 2 . The data may comprise a sensorreading, such as a measurement of a magnetic field of an induction loop,an air pressure measurement, a laser measurement, and/or a radarmeasurement. The data may comprise image data. The image data maycomprise one or more still images. The image data may comprise video.The image may comprise an aerial image, a ground-level image, or aneye-level image. The image may comprise an image captured from a heightand/or position of a camera and/or a traffic control device (e.g., thetraffic control device 104 in FIG. 1 ).

At step 420, a traffic condition may be determined. The trafficcondition may be determined based on the data associated with traffic.The traffic condition may be determined by the computing device. Thetraffic condition may be determined by traffic control device.

The traffic condition may comprise a presence, a number of vehicles, asize (e.g., a length, width, height, weight, etc.), a direction oftravel, and/or a speed of vehicles, such as in the area. The trafficcondition may comprise a wait time of one or more of the vehicles, suchas at an intersection, a stopping point, and/or a traffic controldevice. The traffic condition may comprise a presence of pedestriansand/or number of pedestrians in the area. The traffic condition maycomprise a number of vehicles that are autonomous and/or non-autonomousin the area. The traffic condition may comprise a ratio and/orcomparison of the vehicles that are autonomous and the vehicles that arenon-autonomous in the area. The number of vehicles that are autonomousand/or non-autonomous in the area may be determined using one or more ofthe methods described in step 320 of method 300 in FIG. 3 . The trafficcondition may comprise historical data. The historical data may comprisetraffic data related to time, such as a number of vehicles in an areaand/or vehicle flow over a period of time or at different times of day.

The traffic condition may comprise a number of autonomous vehicles inthe area that are operating in a manned mode and/or a number ofautonomous vehicles in the area that are operating in an unmanned mode.The traffic condition may comprise a ratio and/or comparison of theautonomous vehicles that are operating in a manned mode and theautonomous vehicles that are operating in an unmanned mode in the area.The number of vehicles in the area that are operating in a manned modeand/or the number of vehicles in the area that are operating in anunmanned mode may be determined based on one or more signals receivedfrom one or more of the vehicles. For example, a signal may be receivedfrom one or more of the vehicles indicating that they are operating in amanned mode and/or an unmanned mode. The number of vehicles in the areathat are operating in a manned mode and/or the number of vehicles in thearea that are operating in an unmanned mode may be determined based oncommunications received from a computing device (e.g., an autonomousvehicle network computer), and/or the image data. The signal may bereceived in response to a request sent to the vehicle.

The traffic condition may comprise vehicle movement phases. A phase maycomprise a vector indicating a direction of movement of one or morevehicles. A phase may be associated with traffic in a direction. Forexample, on a road with traffic going in two opposite directions, theremay be two phases. At a T-junction, there may be three phases. Phasesmay also be determined for non-vehicle movement, such as pedestrianmovement, tram movement, and/or bicycle movement. The determination ofthe traffic control command may comprise determining a stage. A stagemay comprise a group of phases, such as non-conflicting phases.

Priorities may be determined for the phases. For example, on a two-waystreet, a phase associated with one direction may be given a firstpriority and a phase associated with traffic in another direction (e.g.,an opposite direction) may be given a second priority. At a junction oran intersection, a first phase may be given a first priority, a secondphase may e given a second priority, a third phase may be given a thirdpriority, etc.

The priorities of the phases may be determined based on a number ofvehicles associated with one or more phases. A priority may bedetermined based on a number of autonomous vehicles associated with thephase. A priority may be determined based on a number of non-autonomousvehicles associated with the phase. A priority may be determined basedon a number of autonomous vehicles operating in a manned mode associatedwith the phase. A priority may be determined based on a number ofautonomous vehicles operating in an unmanned mode associated with thephase. For example, a phase having more vehicles or more of a type ofvehicles than another phase may be given a higher priority or a lowerpriority than the other phase.

The priorities of the phases may be determined based on a velocity ofone or more vehicles associated with a phase. A priority may bedetermine based on a wait time of vehicles associated with the phase.The priority may be determine based on a time of day. The priority maybe determined based on historical data. For example, it may bedetermined that there is more traffic in a direction on a roadway at atime than at other times. A phase associated with the increased trafficmay be given a higher priority than other phases.

Determining the traffic condition may comprise generating a map ofautonomous vehicles or a trajectory of one or more vehicles (e.g., basedon velocity of one or more vehicles). Determining the traffic conditionmay comprise generating a time-space diagram for autonomous vehiclesand/or non-autonomous vehicles, such as based on the positions,velocities, accelerations of the vehicles.

At step 430, a traffic control command may be determined. The trafficcontrol command may be determined based on the traffic condition. Thetraffic control command may be determined based on the data associatedwith the traffic. The traffic control command may be associated with thearea, such as for vehicles and/or pedestrians in the area. The trafficcontrol command may comprise a stop command (e.g., a red light), a gocommand (e.g., a green light), a yield command (e.g., a yellow light ora flashing light), a cross command, and/or a slow command, as examples.The traffic control command may comprise a speed limit. The trafficcontrol command may comprise a warning. As examples, the warning maycomprise a warning about a road closure, an accident, and/orconstruction.

The traffic control command may comprise a duration of a command. Forexample, it may be determined that the a “stop” command should be outputfor a duration of 45 seconds. As an illustrative example, the trafficcondition may comprise a wait time at an intersection having traffic inorthogonal paths. The traffic condition may comprise a number ofvehicles in each path. If a number of vehicles and/or a wait timeexceeds a threshold value, a determination may be made to give trafficin one of the paths a “go” command and give traffic in the other path a“stop” command. The determination may be made based on a weightedfunction (e.g., an average or a sum). For example, variables of theweighted average or sum may comprise a number of vehicles and/or a waittime. A path that has more autonomous vehicles (e.g., a total number ofautonomous vehicles, a ratio of autonomous vehicles to non-autonomousvehicles, etc.) may be given priority (e.g., given a right-of-way or a“go” command). Alternatively, a path that has more non-autonomousvehicles may have priority. Alternatively or additionally, pedestriansmay have priority.

As an example, if a number of autonomous vehicles, non-autonomousvehicles, autonomous vehicles operating in a manned mode, and/orvehicles operating in an unmanned mode, the traffic control command maybe determined. For example, if the number meets or exceeds a thresholdnumber, a first command may be determined. If the number is less thanthe threshold number, a second command may be determined. As anillustrative example, if there are five or more non-autonomous vehiclesin the area, a “go” command may be determined.

The determination of the traffic control command may be adaptive. Forexample, the traffic control commands may be determined based on currenttraffic density and/or flow. The determination of the traffic controlcommand may be made using an algorithm configured to minimize delaysand/or congestion of vehicle traffic. The determination of the trafficcontrol command may be made using an algorithm configured to maximizethe flow of traffic, such as the intelligent driver model (IDM),

The determination of the traffic control command may comprise using apredictive model or algorithm. For example, based on historical data, itmay be determined that there is more traffic on a road or in onedirection at a time of day (e.g., period of time) than other times ofday. It may be determined that a “go” command should be output morefrequently during that time. As another example, based on a map ofautonomous vehicles or based on a trajectory of one or more vehicles(e.g., based on velocity of one or more vehicles), it may be determinedthat one or more vehicles will be at one location at a time. The trafficcontrol command may be determined based on the trajectory. Based on atime-space diagram, the traffic control command may be determined.

Alternatively, step 420 may be omitted from method 400. For example, theif the traffic data is indicative of the traffic condition, step 430 maybe performed without performing step 420. As another example, in step430, the traffic control command may be determined based on the trafficdata and step 420 may be bypassed.

At step 440, an indication of the traffic control command may be output.The indication of the traffic control command may be output by thecomputing device and/or the traffic control device. The indication ofthe traffic control command may be sent to one or more autonomousvehicles. The indication of the traffic control command may be sent toan autonomous vehicle via wireless communication. The indication of thetraffic control command may be sent to an autonomous vehicle via abeacon. Based on the traffic control command, the autonomous vehicle maydetermine and/or update a driving pattern or driving decision. Theautonomous vehicle may send an indication of the traffic control commandto a cloud computing network that aggregates autonomous vehicle dataand/or communicates with autonomous vehicles.

The indication of the traffic control command may be sent to the cloudcomputing network, such as by the computing device and/or the trafficcontrol device. The cloud computing network may aggregate the trafficcontrol command with other traffic data. Based on the aggregated data,the cloud computing network may generate a map. One or more autonomousvehicles may receive the map. The autonomous vehicle may make a trafficdecision and/or follow a traffic pattern based on the map. Based on theaggregated data, the cloud computing network may determine a trafficdecision and/or a traffic pattern for the autonomous vehicle. Theautonomous vehicle may receive an indication of the traffic decisionand/or the traffic pattern and may obey the traffic decisions and/or thetraffic pattern.

The indication of the traffic control command may be output to one ormore operators of non-autonomous vehicles. The indication of the trafficcontrol command may be output via a display module (e.g., display module107 in FIG. 1 ) and/or an audio module (e.g., audio module 108 in FIG. 1). The indication of the traffic control command may be output by thetraffic control device. If the indication of the traffic control commandcomprises a light, an intensity of the light may be determined and/ormodified, such as based on time of day, weather, ambient light, locationof the traffic control device (e.g., relative to another traffic controldevice, relative to one or more vehicles, height above ground level,etc.), and/or other factors. The operator may make traffic decisionsand/or determine traffic patterns based on the traffic control command.

An indication of the traffic control command may be output to anoperator of a non-autonomous vehicle (or an operator of a non-autonomousvehicle operating in a manned mode) and an indication of the trafficcontrol command may be output to an autonomous vehicle (or an autonomousvehicle operating in an unmanned mode) simultaneously. Simultaneouslyoutputting the indication of the traffic control command to the operatorof the non-autonomous vehicle and outputting the indication of thetraffic control command to the autonomous vehicle may compriseoutputting the commands at the same time, within 0.05 second, within 0.1second, within 0.2 second, within 0.5 second, within 1 second, within 5seconds, or within 10 seconds, as examples. Communicating the trafficcontrol command to autonomous vehicles and to operators ofnon-autonomous vehicles may align traffic between non-autonomousvehicles and autonomous vehicles. Communicating the traffic controlcommand to autonomous vehicles operating in an unmanned mode and tooperators of autonomous vehicles operating in a manned mode may aligntraffic between autonomous vehicles operating in an unmanned mode andautonomous vehicles operating in a manned mode.

Traffic control commands output by traffic control devices may becoordinated. For example, a computing device or traffic control devicemay communicate traffic control commands for traffic control devices tooutput to cause a cascade of “go” commands or green lights on a roadway.The “go” commands or green lights may be output based on a sequence oftraffic control devices on a roadway. The cascade may enable vehicles toproceed through a series of “go” commands or green lights. The commandsfor each traffic control device to output may be determined based onspacing between the traffic control devices and/or vehicle speeds and/orvelocities (e.g., expected speeds and/or velocities).

The indication of the traffic control command may be output by onetraffic control device, such as a traffic control device at the areaassociated with the command, to another traffic control device. Forexample, a traffic control device may send an indication of the trafficcontrol command to a traffic control device farther down a road. Asanother example, a traffic control device at a first intersection maysend an indication of the traffic control command to a traffic controldevice at a next intersection. The other traffic control device maydetermine a traffic control command based on the received indication ofthe traffic control command. For example, the traffic control devicesmay communicate and cooperate to make traffic control decisions thatminimize traffic delays and increase the flow of traffic.

The traffic control device may determine to move and/or be caused tomove. The traffic control device determine to move and/or be caused tomove based on the traffic data, the traffic condition, and/or thetraffic control command. The traffic control device may determine tomove and/or be caused to move based on a location of another trafficcontrol device. For example, the traffic control devices may beconfigured to be located a distance apart. If one traffic control devicemoves, another traffic control device may move to maintain the spacing.The traffic control device may determine to move and/or be caused tomove based on a density of traffic in the area. For example, the trafficcontrol device may move to an area having a greater density of trafficthan another area. The traffic control device may be configured to moveon ground, in the air, or on a cable, as examples.

FIG. 5 shows an example computing environment. The systems, methods, andapparatuses described herein may be implemented on a computing devicesuch as a computing device 501 (e.g., computer) as shown in FIG. 5 anddescribed below. The computing device 102 and/or the traffic controldevice 104 in FIG. 1 may be and/or comprise a computing device as shownin FIG. 5 . Similarly, the methods, systems, and apparatuses disclosedmay utilize one or more computing device to perform one or morefunctions in one or more locations. This operating environment is notintended to suggest any limitation as to the scope of use orfunctionality of operating environment architecture. Neither should theoperating environment be interpreted as having any dependency orrequirement relating to any one or combination of components shown inthe operating environment.

The systems, methods, and apparatuses described herein may beoperational with numerous other general purpose or special purposecomputing system environments or configurations. Computing systems,environments, and/or configurations that may be suitable for use withthe systems, methods, and apparatuses comprise, but are not limited to,personal computers, server computers, laptop devices, and multiprocessorsystems. Set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat comprise any of the above systems or devices, and the like may beused to implement the methods, systems, and apparatuses.

The systems, methods, and apparatuses may be implemented, in whole or inpart, by software components. The disclosed methods, systems, andapparatuses may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by one or more computers or other devices. Program modulescomprise computer code, routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. The methods, systems, and apparatuses may bepracticed in grid-based and distributed computing environments wheretasks are performed by remote processing devices that are linked througha communications network. In a distributed computing environment,program modules may be located in both local and remote computer storagemedia including memory storage devices.

The methods, systems, and apparatuses may be implemented via ageneral-purpose computing device in the form of a computing device 501.The components of the computing device 501 may comprise, but are notlimited to, one or more processors 503, a system memory 512, and asystem bus 513 that couples various system components including theprocessor 503 to the system memory 512. With multiple processors 503,the system may utilize parallel computing.

The system bus 513 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. Such architectures maycomprise an Industry Standard Architecture (ISA) bus, a Micro ChannelArchitecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video ElectronicsStandards Association (VESA) local bus, an Accelerated Graphics Port(AGP) bus, and a Peripheral Component Interconnects (PCI), a PCI-Expressbus, a Personal Computer Memory Card Industry Association (PCMCIA),Universal Serial Bus (USB) and the like. The bus 513, and all busesspecified in this description may be implemented over a wired orwireless network connection and each of the subsystems, including theprocessor 503, a mass storage device 504, an operating system 505, datadistillation software 506, data distillation data 507, a network adapter508, system memory 512, an Input/Output Interface 510, a display adapter509, a display device 511, and a human machine interface 502, may becontained within one or more remote computing devices 514 a,b,c atphysically separate locations, connected through buses of this form, ineffect implementing a fully distributed system.

The computing device 501 typically comprises a variety of computerreadable media. Readable media may be any available media that isaccessible by the computing device 501 and comprises both volatile andnon-volatile media, removable and non-removable media. The system memory512 comprises computer readable media in the form of volatile memory,such as random-access memory (RAM), and/or non-volatile memory, such asread only memory (ROM). The system memory 512 typically contains datasuch as data distillation data 507 and/or program modules such asoperating system 505 and data distillation software 506 that areimmediately accessible to and/or are presently operated on by theprocessor 503.

The computing device 501 may comprise other removable/non-removable,volatile/non-volatile computer storage media. FIG. 5 shows a massstorage device 504 which may provide non-volatile storage of computercode, computer readable instructions, data structures, program modules,and other data for the computing device 501. A mass storage device 504may be a hard disk, a removable magnetic disk, a removable optical disk,magnetic cassettes or other magnetic storage devices, flash memorycards, CD-ROM, digital versatile disks (DVD) or other optical storage,random access memories (RAM), read only memories (ROM), electricallyerasable programmable read-only memory (EEPROM), and the like.

Any number of program modules may be stored on the mass storage device504, including an operating system 505 and data distillation software506. Each of the operating system 505 and data distillation software 506(or some combination thereof) may comprise elements of the programmingand the data distillation software 506. Data distillation data 507 maybe stored on the mass storage device 504. Data distillation data 507 maybe stored in any of one or more databases known in the art. Suchdatabases may comprise, DB2®, Microsoft® Access, Microsoft® SQL Server,Oracle®, mySQL, PostgreSQL, and the like. The databases may becentralized or distributed across multiple systems.

The user may enter commands and information into the computing device501 via an input device (not shown). Input devices may comprise, but arenot limited to, a keyboard, pointing device (e.g., a “mouse”), amicrophone, a joystick, tactile input devices such as gloves, and otherbody coverings, and the like. These and other input devices may beconnected to the processor 503 via a human machine interface 502 that iscoupled to the system bus 513 but may be connected by other interfaceand bus structures, such as a parallel port, game port, an IEEE 594 Port(also known as a Firewire port), a serial port, or a universal serialbus (USB).

A display device 511 may be connected to the system bus 513 via aninterface, such as a display adapter 509. It is contemplated that thecomputing device 501 may have more than one display adapter 509 and thecomputing device 501 may have more than one display device 511. Adisplay device may be a monitor, an LCD (Liquid Crystal Display), or aprojector. Output peripheral devices may comprise components such asspeakers (not shown) and a printer (not shown) which may be connected tothe computing device 501 via Input/Output Interface 510. Any step and/orresult of the methods may be output in any form to an output device.Such output may be any form of visual representation, including, but notlimited to, textual, graphical, animation, audio, tactile, and the like.The display 511 and computing device 501 may be part of one device, orseparate devices.

The computing device 501 may operate in a networked environment usinglogical connections to one or more remote computing devices 514 a,b,c. Aremote computing device may be a personal computer, portable computer,smartphone, a server, a router, a network computer, a peer device orother common network node, and so on. Logical connections between thecomputing device 501 and a remote computing device 514 a,b,c may be madevia a network 515, such as a local area network (LAN) and a general widearea network (WAN). Such network connections may be through a networkadapter 508. A network adapter 508 may be implemented in both wired andwireless environments. Such networking environments are conventional andcommonplace in dwellings, offices, enterprise-wide computer networks,intranets, and the Internet.

Application programs and other executable program components such as theoperating system 505 are shown herein as discrete blocks, although it isrecognized that such programs and components reside at various times indifferent storage components of the computing device 501 and areexecuted by the data processor(s) of the computer. An implementation ofdata distillation software 506 may be stored on or transmitted acrosssome form of computer readable media. Any of the disclosed methods maybe performed by computer readable instructions embodied on computerreadable media. Computer readable media may be any available media thatmay be accessed by a computer. Computer readable media may comprise“computer storage media” and “communications media.” “Computer storagemedia” comprise volatile and non-volatile, removable and non-removablemedia implemented in any methods or technology for storage ofinformation such as computer readable instructions, data structures,program modules, or other data. Computer storage media may comprise, butis not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which may be used tostore the desired information, and which may be accessed by a computer.

What is claimed is:
 1. A method comprising: receiving vehicle data froma plurality of vehicles operating autonomously; determining, based onthe vehicle data, one or more characteristics of the plurality ofvehicles operating autonomously; generating, based on the one or morecharacteristics and traffic data, one or more maps; receiving, by avehicle of the plurality of vehicles operating autonomously, the one ormore generated maps; determining, based on the traffic data, a trafficcondition; determining, based on the traffic condition and the one ormore determined characteristics, a traffic control command; outputting,by a traffic control device, a first indication, configured for the oneor more vehicles operating autonomously, of the traffic control command,wherein the vehicle of the plurality of vehicles operating autonomouslydetermines to obey the traffic control command based on the received oneor more generated map; and outputting, by the traffic control device, asecond indication, configured for one or more vehicle operators, of thetraffic control command.
 2. The method of claim 1, wherein the trafficdata is received from at least one of a vehicle, a street sensor, oranother traffic control device.
 3. The method of claim 1, wherein thetraffic data comprises image data.
 4. The method of claim 1, wherein thetraffic data comprises sensor data.
 5. The method of claim 1, whereinthe traffic condition comprises a number of vehicles in a traffic area.6. The method of claim 1, wherein the traffic condition comprise anumber of vehicles operating autonomously and a number of vehiclesoperating in a non-autonomous mode.
 7. The method of claim 1, whereinthe second indication comprises an audio indication.
 8. The method ofclaim 1, wherein the traffic condition is associated with a trafficarea; and wherein at least one vehicle of the one or more vehicles islocated outside the traffic area.
 9. The method of claim 1, wherein thetraffic control command comprises a command to operate in anon-autonomous mode.
 10. The method of claim 1, wherein the trafficcontrol command comprises at least one of a stop command, a go command,or a slow command.
 11. The method of claim 1, wherein the determiningthe traffic control command comprises determining a first trafficcontrol command associated with the one or more autonomous vehicles anddetermining a second traffic control command associated with vehiclesoperating in a manned mode; and wherein the first indication of thetraffic control command comprises an indication of the first trafficcontrol command and the second indication of the traffic control commandcomprises an indication of the second traffic control command.
 12. Atraffic control device comprising: one or more processors; and memorystoring instructions that, when executed by the one or more processors,causes the traffic control device to: receive vehicle data from aplurality of vehicles operating autonomously; determine, based on thevehicle data, one or more characteristics of the plurality of vehiclesoperating autonomously; generate, based on the one or morecharacteristics and traffic data, one or more maps; receive, by avehicle of the plurality of vehicles operating autonomously, the one ormore generated maps; determine, based on the traffic data, a trafficcondition; determine, based on the traffic condition and the one or moredetermined characteristics, a traffic control command; output a firstindication, configured for the one or more vehicles operatingautonomously, of the traffic control command, wherein the vehicle of theplurality of vehicles operating autonomously determines to obey thetraffic control command based on the received one or more generated map;and output, by the traffic control device, a second indication,configured for one or more vehicle operators, of the traffic controlcommand.
 13. The traffic control device of claim 12, wherein the trafficcontrol device comprises a mobile device.
 14. The traffic control deviceof claim 12, wherein the traffic control device comprises a self-drivingdevice.
 15. A non-transitory computer-readable medium comprisinginstructions that, when executed, cause operations comprising: receivingvehicle data from a plurality of vehicles operating autonomously;determining, based on the vehicle data, one or more characteristics ofthe plurality of vehicles operating autonomously; generating, based onthe one or more characteristics and traffic data, one or more maps;receiving, by a vehicle of the plurality of vehicles operatingautonomously, the one or more generated maps; determining, based on thetraffic data, a traffic condition; determining, based on the trafficcondition and the one or more determined characteristics, a trafficcontrol command; outputting a first indication, configured for the oneor more vehicles operating autonomously, of the traffic control command,wherein the vehicle of the plurality of vehicles operating autonomouslydetermines to obey the traffic control command based on the received oneor more generated map; and outputting a second indication, configuredfor one or more vehicle operators, of the traffic control command. 16.The transitory computer-readable medium of claim 15, wherein the trafficdata, the traffic condition, and the traffic control command areassociated with a traffic area.
 17. The transitory computer-readablemedium of claim 15, wherein the traffic condition comprises at least oneof a size, a speed, an acceleration, or a direction of travel of avehicle.
 18. The transitory computer-readable medium of claim 15,wherein the traffic control command comprises an indication of adirection of travel.
 19. The transitory computer-readable medium ofclaim 15, wherein the second indication comprises a visual indication.20. The transitory computer-readable medium of claim 15, wherein thesecond indication of the traffic control command is configured to causea vehicle operating in a manned mode to operate in an unmanned mode.